Development and application of a metabolomic tool to assess exposure of an estuarine amphipod to pollutants in the environment

Abstract

Identifying substances in sediment that cause adverse effects on benthic organisms has been implemented as an effective source-control strategy. However, the identification of such substances is difficult due to the complicated interactions between liquid and solid phases and organisms. Metabolomic approaches have been utilized to assess the effects of toxicants on various organisms; however, the relationships between the toxicants and metabolomic profiles have not been generalized, and it makes impractical to identify major toxicants from metabolomic information. In this study, we used partial least squares discriminant analysis (PLS-DA) to investigate these relationships. The objective of this study was to construct PLS-DA models using the metabolomic profiles of the benthic amphipod Grandidierella japonica to assess the exposure effects of target toxicants and to demonstrate the utility of these models to assess the effects of chemicals in environmental samples. The PLS-DA models were constructed from the metabolomic profiles of G. japonica to discriminate the exposure of G. japonica to chromium, nickel, copper, zinc, cadmium, fluoranthene, nicotine, and osmotic stress. The models had high predictive power for the presumptive exposure to each chemical and were able to detect exposures in mixed chemical samples. These results suggest that the metabolomic responses can provide important information for the assessment of chemical effects on organisms. We applied the models to the metabolomic profiles of G. japonica exposed to river sediment and road dust, and the results demonstrated the applicability of the models. The control groups were not classified as exposure groups, and no samples were presumed to belong to any exposure group. These results suggest that the target chemicals were not toxic in the samples and conditions we investigated. This study demonstrates a method to assess the relationships between chemical exposure and metabolomic responses. To the best our knowledge, this is the first study to apply metabolomics for identification of toxicants.